GPT-5 vs GPT-5.5
Compare GPT-5 and GPT-5.5 side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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GPT-5 vs GPT-5.5 Comparison Table
Evals updated September 5, 2026Pricing updated September 21, 2026
| Property | GPT-5 | GPT-5.5 |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Apr 2026 |
| Context Window | — | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.25 | $5.00 |
| Output $/1M | $10.00 | $30.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 74.8% |
| Avg cost / sample | – | $0.022 |
| Avg speed / sample | – | 9.03s |
| By task | ||
| Object Detection (low) | – | 43.6% ±2.2, Mean of 3 runs, range 41.7 to 46.1 |
| Object Detection (high) | – | 44.2% ±0.6, Mean of 3 runs, range 43.5 to 44.8 |
| Counting (low) | – | 68.0% ±3.4, Mean of 3 runs, range 64.9 to 71.6 |
| Counting (high) | – | 68.0% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Identification (low) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | – | 91.2% ±0.3, Mean of 3 runs, range 90.9 to 91.6 |
| OCR (high) | – | 91.7% ±0.6, Mean of 3 runs, range 91.1 to 92.3 |
| Data Extraction (low) | – | 85.9% ±1.5, Mean of 3 runs, range 84.5 to 87.6 |
| Data Extraction (high) | – | 86.9% ±0.5, Mean of 3 runs, range 86.6 to 87.6 |
| Reasoning (low) | – | 70.6% ±1.0, Mean of 3 runs, range 69.5 to 71.5 |
| Reasoning (high) | – | 72.2% ±3.6, Mean of 3 runs, range 68.9 to 76.2 |
GPT-5 vs GPT-5.5: Overview
GPT-5, released by OpenAI in August 2025, is a multimodal large language model that advances beyond the GPT-4 family with a new “unified system” architecture. This design allows the model to dynamically choose between fast responses and extended reasoning depending on task complexity. It supports text, code, and images, alongside stronger tool use and agentic workflows, making it more adaptable for real-world problem solving. While its exact context window size is not disclosed, GPT-5 is optimized for long-horizon reasoning and multi-step tool chaining, indicating substantially expanded capacity over its predecessors.
The release introduced specialized variants: GPT-5 Pro, offering extended reasoning for complex workflows, and GPT-5 Codex, optimized for advanced coding tasks such as large-scale refactoring and code review. GPT-5 shows benchmark gains in coding, biomedical reasoning, multimodal analysis, and scientific tasks. Developers also gain new controls, such as verbosity and personalization parameters, for greater steerability. With these improvements, GPT-5 positions itself as OpenAI’s most capable and versatile model, suited for enterprise automation, research, healthcare, and sophisticated coding environments.
GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.
Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.